H.6.5.2. Computer vision
Kourosh Kiani; Razieh Rastgoo; Alireza Chaji; Sergio Escalera
Abstract
Image inpainting, the process of restoring missing or corrupted regions of an image by reconstructing pixel information, has recently seen considerable advancements through deep learning-based approaches. Aiming to tackle the complex spatial relationships within an image, in this paper, we introduce ...
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Image inpainting, the process of restoring missing or corrupted regions of an image by reconstructing pixel information, has recently seen considerable advancements through deep learning-based approaches. Aiming to tackle the complex spatial relationships within an image, in this paper, we introduce a novel deep learning-based pre-processing methodology for image inpainting utilizing the Vision Transformer (ViT). Unlike CNN-based methods, our approach leverages the self-attention mechanism of ViT to model global contextual dependencies, improving the quality of inpainted regions. Specifically, we replace masked pixel values with those generated by the ViT, utilizing the attention mechanism to extract diverse visual patches and capture discriminative spatial features. To the best of our knowledge, this is the first instance of such a pre-processing model being proposed for image inpainting tasks. Furthermore, we demonstrate that our methodology can be effectively applied using a pre-trained ViT model with a pre-defined patch size, reducing computational overhead while maintaining high reconstruction fidelity. To assess the generalization capability of the proposed methodology, we conduct extensive experiments comparing our approach with four standard inpainting models across four public datasets. The results validate the efficacy of our pre-processing technique in enhancing inpainting performance, particularly in scenarios involving complex textures and large missing regions.
H.5.11. Image Representation
E. Sahragard; H. Farsi; S. Mohammadzadeh
Abstract
The aim of image restoration is to obtain a higher quality desired image from a degraded image. In this strategy, an image inpainting method fills the degraded or lost area of the image by appropriate information. This is performed in such a way so that the obtained image is undistinguishable for a casual ...
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The aim of image restoration is to obtain a higher quality desired image from a degraded image. In this strategy, an image inpainting method fills the degraded or lost area of the image by appropriate information. This is performed in such a way so that the obtained image is undistinguishable for a casual person who is unfamiliar with the original image. In this paper, different images are degraded by two procedures; one is to blur and to add noise to the original image, and the other one is to lose a percentage of the pixels belonging to the original image. Then, the degraded image is restored by the proposed method and also two state-of-art methods. For image restoration, it is required to use optimization methods. In this paper, we use a linear restoration method based on the total variation regularizer. The variable of optimization problem is split, and the new optimization problem is solved by using Lagrangian augmented method. The experimental results show that the proposed method is faster, and the restored images have higher quality compared to the other methods.
